Overview

Dataset statistics

Number of variables12
Number of observations999
Missing cells410
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory697.0 KiB
Average record size in memory714.4 B

Variable types

Numeric6
Text2
DateTime1
URL1
Categorical1
Unsupported1

Alerts

complexity has 410 (41.0%) missing valuesMissing
views is highly skewed (γ1 = 27.7391176)Skewed
id has unique valuesUnique
published_datetime has unique valuesUnique
title has unique valuesUnique
url has unique valuesUnique
tags is an unsupported type, check if it needs cleaning or further analysisUnsupported
votes has 52 (5.2%) zerosZeros
comments has 112 (11.2%) zerosZeros

Reproduction

Analysis started2024-03-01 13:23:58.124587
Analysis finished2024-03-01 13:24:04.244923
Duration6.12 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean742771.95
Minimum599113
Maximum796921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-01T16:24:04.374576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum599113
5-th percentile687171.4
Q1713091
median745708
Q3772800
95-th percentile792079.2
Maximum796921
Range197808
Interquartile range (IQR)59709

Descriptive statistics

Standard deviation34619.835
Coefficient of variation (CV)0.046608969
Kurtosis-0.97963001
Mean742771.95
Median Absolute Deviation (MAD)30062
Skewness-0.2126356
Sum7.4202918 × 108
Variance1.1985329 × 109
MonotonicityNot monotonic
2024-03-01T16:24:04.588005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
796847 1
 
0.1%
722962 1
 
0.1%
724314 1
 
0.1%
724098 1
 
0.1%
723600 1
 
0.1%
723870 1
 
0.1%
722108 1
 
0.1%
723728 1
 
0.1%
723702 1
 
0.1%
723030 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
599113 1
0.1%
660697 1
0.1%
679542 1
0.1%
679952 1
0.1%
680240 1
0.1%
680382 1
0.1%
680670 1
0.1%
680770 1
0.1%
681284 1
0.1%
681398 1
0.1%
ValueCountFrequency (%)
796921 1
0.1%
796847 1
0.1%
796793 1
0.1%
796669 1
0.1%
796633 1
0.1%
796579 1
0.1%
796385 1
0.1%
796335 1
0.1%
796169 1
0.1%
796143 1
0.1%

author
Text

Distinct564
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size64.6 KiB
2024-03-01T16:24:04.851388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.0870871
Min length3

Characters and Unicode

Total characters9078
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)42.2%

Sample

1st rowParker0
2nd rowAnastasiaIvanova8
3rd rowEgorzaa
4th rowwinfox_tech
5th rowWolodyaCh
ValueCountFrequency (%)
aio350 55
 
5.5%
gmtd 18
 
1.8%
mr-pickles 17
 
1.7%
sergeytolkachyov 14
 
1.4%
melnik909 13
 
1.3%
simbirsoft_frontend 12
 
1.2%
nin-jin 10
 
1.0%
rostislavdugin 10
 
1.0%
qmzik 10
 
1.0%
parker0 9
 
0.9%
Other values (554) 831
83.2%
2024-03-01T16:24:05.327110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 829
 
9.1%
o 666
 
7.3%
i 639
 
7.0%
e 628
 
6.9%
r 514
 
5.7%
n 505
 
5.6%
s 398
 
4.4%
t 392
 
4.3%
l 371
 
4.1%
k 350
 
3.9%
Other values (54) 3786
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7610
83.8%
Uppercase Letter 808
 
8.9%
Decimal Number 463
 
5.1%
Connector Punctuation 149
 
1.6%
Dash Punctuation 48
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 829
 
10.9%
o 666
 
8.8%
i 639
 
8.4%
e 628
 
8.3%
r 514
 
6.8%
n 505
 
6.6%
s 398
 
5.2%
t 392
 
5.2%
l 371
 
4.9%
k 350
 
4.6%
Other values (16) 2318
30.5%
Uppercase Letter
ValueCountFrequency (%)
S 96
 
11.9%
M 59
 
7.3%
A 54
 
6.7%
B 52
 
6.4%
D 51
 
6.3%
P 49
 
6.1%
G 49
 
6.1%
L 48
 
5.9%
R 38
 
4.7%
T 37
 
4.6%
Other values (16) 275
34.0%
Decimal Number
ValueCountFrequency (%)
0 111
24.0%
3 80
17.3%
5 72
15.6%
1 54
11.7%
9 40
 
8.6%
2 36
 
7.8%
4 20
 
4.3%
7 19
 
4.1%
6 16
 
3.5%
8 15
 
3.2%
Connector Punctuation
ValueCountFrequency (%)
_ 149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8418
92.7%
Common 660
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 829
 
9.8%
o 666
 
7.9%
i 639
 
7.6%
e 628
 
7.5%
r 514
 
6.1%
n 505
 
6.0%
s 398
 
4.7%
t 392
 
4.7%
l 371
 
4.4%
k 350
 
4.2%
Other values (42) 3126
37.1%
Common
ValueCountFrequency (%)
_ 149
22.6%
0 111
16.8%
3 80
12.1%
5 72
10.9%
1 54
 
8.2%
- 48
 
7.3%
9 40
 
6.1%
2 36
 
5.5%
4 20
 
3.0%
7 19
 
2.9%
Other values (2) 31
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 829
 
9.1%
o 666
 
7.3%
i 639
 
7.0%
e 628
 
6.9%
r 514
 
5.7%
n 505
 
5.6%
s 398
 
4.4%
t 392
 
4.3%
l 371
 
4.1%
k 350
 
3.9%
Other values (54) 3786
41.7%

published_datetime
Date

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2022-08-02 09:21:40+00:00
Maximum2024-02-29 07:00:54+00:00
2024-03-01T16:24:05.525581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:05.705133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

title
Text

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size278.7 KiB
2024-03-01T16:24:05.942670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length120
Median length90
Mean length55.296296
Min length15

Characters and Unicode

Total characters55241
Distinct characters166
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique999 ?
Unique (%)100.0%

Sample

1st rowChromium. Отрисовка страницы с помощью Blink, CC и планировщика
2nd rowСоздание WordPress-плагина для отправки SMS-сообщений
3rd rowКак разработать браузерное расширение в Chrome на React: разбираем на примере Cloudhood
4th rowSkunk Works курильщика, или собственный лоу-код на страже продаж
5th rowTelegram Mini Apps с мгновенной оплатой
ValueCountFrequency (%)
в 299
 
3.7%
и 274
 
3.4%
как 214
 
2.7%
с 167
 
2.1%
на 166
 
2.1%
для 123
 
1.5%
75
 
0.9%
часть 59
 
0.7%
react 59
 
0.7%
не 58
 
0.7%
Other values (3464) 6519
81.4%
2024-03-01T16:24:06.396488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6995
 
12.7%
о 3583
 
6.5%
а 3342
 
6.0%
и 3079
 
5.6%
е 3045
 
5.5%
т 2449
 
4.4%
н 2252
 
4.1%
р 2169
 
3.9%
с 1796
 
3.3%
в 1553
 
2.8%
Other values (156) 24978
45.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42937
77.7%
Space Separator 7016
 
12.7%
Uppercase Letter 3395
 
6.1%
Other Punctuation 820
 
1.5%
Decimal Number 519
 
0.9%
Dash Punctuation 344
 
0.6%
Math Symbol 57
 
0.1%
Close Punctuation 43
 
0.1%
Open Punctuation 43
 
0.1%
Final Punctuation 30
 
0.1%
Other values (4) 37
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
о 3583
 
8.3%
а 3342
 
7.8%
и 3079
 
7.2%
е 3045
 
7.1%
т 2449
 
5.7%
н 2252
 
5.2%
р 2169
 
5.1%
с 1796
 
4.2%
в 1553
 
3.6%
к 1476
 
3.4%
Other values (56) 18193
42.4%
Uppercase Letter
ValueCountFrequency (%)
S 389
 
11.5%
К 205
 
6.0%
P 186
 
5.5%
C 170
 
5.0%
T 161
 
4.7%
R 144
 
4.2%
A 135
 
4.0%
П 113
 
3.3%
M 100
 
2.9%
С 98
 
2.9%
Other values (42) 1694
49.9%
Other Punctuation
ValueCountFrequency (%)
. 244
29.8%
, 228
27.8%
: 215
26.2%
? 75
 
9.1%
/ 21
 
2.6%
8
 
1.0%
& 7
 
0.9%
! 6
 
0.7%
% 5
 
0.6%
' 3
 
0.4%
Other values (5) 8
 
1.0%
Decimal Number
ValueCountFrequency (%)
2 140
27.0%
0 112
21.6%
1 90
17.3%
3 69
13.3%
4 34
 
6.6%
5 23
 
4.4%
6 17
 
3.3%
7 15
 
2.9%
8 12
 
2.3%
9 7
 
1.3%
Math Symbol
ValueCountFrequency (%)
+ 42
73.7%
= 5
 
8.8%
| 3
 
5.3%
> 2
 
3.5%
< 2
 
3.5%
2
 
3.5%
~ 1
 
1.8%
Space Separator
ValueCountFrequency (%)
6995
99.7%
  19
 
0.3%
2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 263
76.5%
75
 
21.8%
6
 
1.7%
Close Punctuation
ValueCountFrequency (%)
) 41
95.3%
] 2
 
4.7%
Open Punctuation
ValueCountFrequency (%)
( 41
95.3%
[ 2
 
4.7%
Final Punctuation
ValueCountFrequency (%)
» 28
93.3%
2
 
6.7%
Initial Punctuation
ValueCountFrequency (%)
« 28
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 4
100.0%
Other Symbol
ValueCountFrequency (%)
3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 37798
68.4%
Common 8916
 
16.1%
Latin 8527
 
15.4%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
о 3583
 
9.5%
а 3342
 
8.8%
и 3079
 
8.1%
е 3045
 
8.1%
т 2449
 
6.5%
н 2252
 
6.0%
р 2169
 
5.7%
с 1796
 
4.8%
в 1553
 
4.1%
к 1476
 
3.9%
Other values (49) 13054
34.5%
Common
ValueCountFrequency (%)
6995
78.5%
- 263
 
2.9%
. 244
 
2.7%
, 228
 
2.6%
: 215
 
2.4%
2 140
 
1.6%
0 112
 
1.3%
1 90
 
1.0%
? 75
 
0.8%
75
 
0.8%
Other values (45) 479
 
5.4%
Latin
ValueCountFrequency (%)
e 830
 
9.7%
a 579
 
6.8%
t 552
 
6.5%
o 469
 
5.5%
r 457
 
5.4%
i 391
 
4.6%
S 389
 
4.6%
n 351
 
4.1%
c 316
 
3.7%
s 312
 
3.7%
Other values (42) 3881
45.5%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 37798
68.4%
ASCII 17261
31.2%
Punctuation 93
 
0.2%
None 77
 
0.1%
Math Alphanum 7
 
< 0.1%
Letterlike Symbols 3
 
< 0.1%
Arrows 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6995
40.5%
e 830
 
4.8%
a 579
 
3.4%
t 552
 
3.2%
o 469
 
2.7%
r 457
 
2.6%
i 391
 
2.3%
S 389
 
2.3%
n 351
 
2.0%
c 316
 
1.8%
Other values (79) 5932
34.4%
Cyrillic
ValueCountFrequency (%)
о 3583
 
9.5%
а 3342
 
8.8%
и 3079
 
8.1%
е 3045
 
8.1%
т 2449
 
6.5%
н 2252
 
6.0%
р 2169
 
5.7%
с 1796
 
4.8%
в 1553
 
4.1%
к 1476
 
3.9%
Other values (49) 13054
34.5%
Punctuation
ValueCountFrequency (%)
75
80.6%
8
 
8.6%
6
 
6.5%
2
 
2.2%
2
 
2.2%
None
ValueCountFrequency (%)
« 28
36.4%
» 28
36.4%
  19
24.7%
2
 
2.6%
Letterlike Symbols
ValueCountFrequency (%)
3
100.0%
Arrows
ValueCountFrequency (%)
2
100.0%
Math Alphanum
ValueCountFrequency (%)
𝚍 1
14.3%
𝚒 1
14.3%
𝚛 1
14.3%
𝚊 1
14.3%
𝚞 1
14.3%
𝚝 1
14.3%
𝚘 1
14.3%

url
URL

UNIQUE 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size98.2 KiB
https://habr.com/ru/articles/796847/
 
1
https://habr.com/ru/articles/722962/
 
1
https://habr.com/ru/companies/sportmaster_lab/articles/724314/
 
1
https://habr.com/ru/articles/724098/
 
1
https://habr.com/ru/companies/timeweb/articles/723600/
 
1
Other values (994)
994 
ValueCountFrequency (%)
https://habr.com/ru/articles/796847/ 1
 
0.1%
https://habr.com/ru/articles/722962/ 1
 
0.1%
https://habr.com/ru/companies/sportmaster_lab/articles/724314/ 1
 
0.1%
https://habr.com/ru/articles/724098/ 1
 
0.1%
https://habr.com/ru/companies/timeweb/articles/723600/ 1
 
0.1%
https://habr.com/ru/articles/723870/ 1
 
0.1%
https://habr.com/ru/companies/timeweb/articles/722108/ 1
 
0.1%
https://habr.com/ru/articles/723728/ 1
 
0.1%
https://habr.com/ru/articles/723702/ 1
 
0.1%
https://habr.com/ru/articles/723030/ 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
https 999
100.0%
ValueCountFrequency (%)
habr.com 999
100.0%
ValueCountFrequency (%)
/ru/articles/796847/ 1
 
0.1%
/ru/articles/722962/ 1
 
0.1%
/ru/companies/sportmaster_lab/articles/724314/ 1
 
0.1%
/ru/articles/724098/ 1
 
0.1%
/ru/companies/timeweb/articles/723600/ 1
 
0.1%
/ru/articles/723870/ 1
 
0.1%
/ru/companies/timeweb/articles/722108/ 1
 
0.1%
/ru/articles/723728/ 1
 
0.1%
/ru/articles/723702/ 1
 
0.1%
/ru/articles/723030/ 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
999
100.0%
ValueCountFrequency (%)
999
100.0%

complexity
Categorical

MISSING 

Distinct3
Distinct (%)0.5%
Missing410
Missing (%)41.0%
Memory size89.6 KiB
Простой
285 
Средний
280 
Сложный
 
24

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4123
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowПростой
2nd rowСредний
3rd rowСредний
4th rowПростой
5th rowСредний

Common Values

ValueCountFrequency (%)
Простой 285
28.5%
Средний 280
28.0%
Сложный 24
 
2.4%
(Missing) 410
41.0%

Length

2024-03-01T16:24:06.559022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T16:24:06.665768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
простой 285
48.4%
средний 280
47.5%
сложный 24
 
4.1%

Most occurring characters

ValueCountFrequency (%)
о 594
14.4%
й 589
14.3%
р 565
13.7%
С 304
7.4%
н 304
7.4%
П 285
6.9%
с 285
6.9%
т 285
6.9%
е 280
6.8%
д 280
6.8%
Other values (4) 352
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3534
85.7%
Uppercase Letter 589
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
о 594
16.8%
й 589
16.7%
р 565
16.0%
н 304
8.6%
с 285
8.1%
т 285
8.1%
е 280
7.9%
д 280
7.9%
и 280
7.9%
л 24
 
0.7%
Other values (2) 48
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
С 304
51.6%
П 285
48.4%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 4123
100.0%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
о 594
14.4%
й 589
14.3%
р 565
13.7%
С 304
7.4%
н 304
7.4%
П 285
6.9%
с 285
6.9%
т 285
6.9%
е 280
6.8%
д 280
6.8%
Other values (4) 352
8.5%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 4123
100.0%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
о 594
14.4%
й 589
14.3%
р 565
13.7%
С 304
7.4%
н 304
7.4%
П 285
6.9%
с 285
6.9%
т 285
6.9%
е 280
6.8%
д 280
6.8%
Other values (4) 352
8.5%

reading_time
Real number (ℝ)

Distinct38
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4014014
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-01T16:24:06.791034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q310
95-th percentile19.1
Maximum62
Range61
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.0980662
Coefficient of variation (CV)0.72583916
Kurtosis11.830899
Mean8.4014014
Median Absolute Deviation (MAD)3
Skewness2.6893284
Sum8393
Variance37.186411
MonotonicityNot monotonic
2024-03-01T16:24:06.933348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5 133
13.3%
4 111
11.1%
6 107
10.7%
7 98
9.8%
8 92
9.2%
3 83
8.3%
10 56
 
5.6%
9 50
 
5.0%
12 33
 
3.3%
2 32
 
3.2%
Other values (28) 204
20.4%
ValueCountFrequency (%)
1 6
 
0.6%
2 32
 
3.2%
3 83
8.3%
4 111
11.1%
5 133
13.3%
6 107
10.7%
7 98
9.8%
8 92
9.2%
9 50
 
5.0%
10 56
5.6%
ValueCountFrequency (%)
62 1
 
0.1%
50 1
 
0.1%
40 1
 
0.1%
37 1
 
0.1%
34 4
0.4%
33 4
0.4%
32 2
0.2%
31 2
0.2%
30 1
 
0.1%
29 3
0.3%

views
Real number (ℝ)

SKEWED 

Distinct166
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11486.579
Minimum358
Maximum1500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-01T16:24:07.088929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum358
5-th percentile1300
Q13200
median5900
Q312000
95-th percentile31100
Maximum1500000
Range1499642
Interquartile range (IQR)8800

Descriptive statistics

Standard deviation49331.206
Coefficient of variation (CV)4.2946824
Kurtosis833.08772
Mean11486.579
Median Absolute Deviation (MAD)3300
Skewness27.739118
Sum11475092
Variance2.4335679 × 109
MonotonicityNot monotonic
2024-03-01T16:24:07.259474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 37
 
3.7%
11000 24
 
2.4%
15000 22
 
2.2%
13000 21
 
2.1%
10000 20
 
2.0%
14000 19
 
1.9%
2200 16
 
1.6%
18000 16
 
1.6%
2800 15
 
1.5%
3100 15
 
1.5%
Other values (156) 794
79.5%
ValueCountFrequency (%)
358 1
0.1%
549 1
0.1%
620 1
0.1%
639 2
0.2%
672 1
0.1%
723 1
0.1%
739 1
0.1%
766 1
0.1%
789 1
0.1%
790 1
0.1%
ValueCountFrequency (%)
1500000 1
0.1%
203000 1
0.1%
186000 1
0.1%
141000 1
0.1%
115000 1
0.1%
84000 1
0.1%
78000 1
0.1%
76000 1
0.1%
71000 2
0.2%
70000 2
0.2%

tags
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size124.6 KiB

votes
Real number (ℝ)

ZEROS 

Distinct94
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.085085
Minimum0
Maximum271
Zeros52
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-01T16:24:07.440988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q317
95-th percentile57.2
Maximum271
Range271
Interquartile range (IQR)14

Descriptive statistics

Standard deviation24.953419
Coefficient of variation (CV)1.5513389
Kurtosis23.997442
Mean16.085085
Median Absolute Deviation (MAD)5
Skewness4.1526078
Sum16069
Variance622.67311
MonotonicityNot monotonic
2024-03-01T16:24:07.627456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 82
 
8.2%
4 72
 
7.2%
2 68
 
6.8%
5 53
 
5.3%
1 52
 
5.2%
0 52
 
5.2%
6 50
 
5.0%
8 43
 
4.3%
7 42
 
4.2%
10 36
 
3.6%
Other values (84) 449
44.9%
ValueCountFrequency (%)
0 52
5.2%
1 52
5.2%
2 68
6.8%
3 82
8.2%
4 72
7.2%
5 53
5.3%
6 50
5.0%
7 42
4.2%
8 43
4.3%
9 33
3.3%
ValueCountFrequency (%)
271 1
0.1%
190 1
0.1%
177 1
0.1%
171 1
0.1%
167 2
0.2%
166 1
0.1%
152 1
0.1%
148 1
0.1%
142 1
0.1%
135 1
0.1%

bookmarks
Real number (ℝ)

Distinct187
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.68969
Minimum2
Maximum747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-01T16:24:07.801640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q121
median38
Q369.5
95-th percentile170.1
Maximum747
Range745
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation61.261601
Coefficient of variation (CV)1.080648
Kurtosis30.793929
Mean56.68969
Median Absolute Deviation (MAD)22
Skewness4.0979867
Sum56633
Variance3752.9838
MonotonicityNot monotonic
2024-03-01T16:24:07.984720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 23
 
2.3%
15 21
 
2.1%
29 19
 
1.9%
32 19
 
1.9%
26 19
 
1.9%
9 18
 
1.8%
19 18
 
1.8%
20 17
 
1.7%
31 17
 
1.7%
22 17
 
1.7%
Other values (177) 811
81.2%
ValueCountFrequency (%)
2 2
 
0.2%
3 5
 
0.5%
4 1
 
0.1%
5 8
0.8%
6 14
1.4%
7 10
1.0%
8 16
1.6%
9 18
1.8%
10 14
1.4%
11 17
1.7%
ValueCountFrequency (%)
747 1
0.1%
643 1
0.1%
578 1
0.1%
324 1
0.1%
297 1
0.1%
294 1
0.1%
281 1
0.1%
280 1
0.1%
279 1
0.1%
273 1
0.1%

comments
Real number (ℝ)

ZEROS 

Distinct108
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.805806
Minimum0
Maximum596
Zeros112
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-01T16:24:08.175210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median7
Q316
95-th percentile77
Maximum596
Range596
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation45.89965
Coefficient of variation (CV)2.440717
Kurtosis55.966033
Mean18.805806
Median Absolute Deviation (MAD)5
Skewness6.5974592
Sum18787
Variance2106.7779
MonotonicityNot monotonic
2024-03-01T16:24:08.361711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
11.2%
2 83
 
8.3%
4 68
 
6.8%
3 67
 
6.7%
5 59
 
5.9%
1 55
 
5.5%
6 48
 
4.8%
7 43
 
4.3%
9 38
 
3.8%
10 35
 
3.5%
Other values (98) 391
39.1%
ValueCountFrequency (%)
0 112
11.2%
1 55
5.5%
2 83
8.3%
3 67
6.7%
4 68
6.8%
5 59
5.9%
6 48
4.8%
7 43
 
4.3%
8 26
 
2.6%
9 38
 
3.8%
ValueCountFrequency (%)
596 1
0.1%
457 1
0.1%
445 1
0.1%
414 1
0.1%
346 1
0.1%
327 1
0.1%
306 1
0.1%
302 1
0.1%
274 1
0.1%
268 1
0.1%

Interactions

2024-03-01T16:24:02.958225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:58.567403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:59.510983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:00.391698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.196626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.979713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:03.106827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:58.746468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:59.668561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:00.556259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.327277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:02.171198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:03.252438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:58.903608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:59.804732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:00.681922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.449951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:02.333765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:03.408021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:59.054206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:59.954867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:00.814106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.586584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:02.505305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:03.540667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:59.200813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:00.088510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:00.936324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.703271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:02.659893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:03.692261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:23:59.357394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:00.241103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.066973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:01.829113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-01T16:24:02.814022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-01T16:24:03.891867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-01T16:24:04.144192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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2793124Egorzaa2024-02-28 10:34:27+00:00Как разработать браузерное расширение в Chrome на React: разбираем на примере Cloudhoodhttps://habr.com/ru/companies/cloud_ru/articles/793124/Простой51200[Блог компании Cloud.ru, Веб-разработка, Open source, IT-инфраструктура]3271
3796633winfox_tech2024-02-28 07:31:55+00:00Skunk Works курильщика, или собственный лоу-код на страже продажhttps://habr.com/ru/articles/796633/Средний6639[Веб-разработка, Программирование, Анализ и проектирование систем]280
4796793WolodyaCh2024-02-28 07:21:10+00:00Telegram Mini Apps с мгновенной оплатойhttps://habr.com/ru/articles/796793/Средний62000[Веб-разработка, Python, API, Облачные сервисы]8530
5796669DimDimDimDimDim2024-02-27 15:55:09+00:00Как быстро написать API на FastAPI с валидацией и базой данныхhttps://habr.com/ru/companies/selectel/articles/796669/NaN119000[Блог компании Selectel, Веб-разработка, Python, API]3413727
6796579denis_voronin_habr2024-02-27 10:20:40+00:00Динамические Breadcrumbs на React, React Router и Apollo GraphQLhttps://habr.com/ru/articles/796579/Простой41000[Веб-разработка, JavaScript, ReactJS, TypeScript]2170
7794410sLio2024-02-26 21:11:11+00:00Сравнение utility types библиотек или тайпскрипт на стероидахhttps://habr.com/ru/articles/794410/NaN122300[Веб-разработка, TypeScript]7288
8796385kmoseenk2024-02-26 15:14:39+00:00Как украсить и оживить сайт на Astro с помощью KwesForms и Rivehttps://habr.com/ru/companies/otus/articles/796385/NaN6549[Блог компании OTUS, Веб-разработка]12120
9796335DenSyo2024-02-26 12:46:45+00:00Концепт бюджетной видеостены неограниченного размера для web-приложенияhttps://habr.com/ru/articles/796335/Средний71700[Веб-разработка, JavaScript, Программирование]3190
idauthorpublished_datetimetitleurlcomplexityreading_timeviewstagsvotesbookmarkscomments
989681866honyaki2022-08-10 20:13:37+00:00Как сократить код Canvas API в Sveltehttps://habr.com/ru/companies/skillfactory/articles/681866/NaN102600[Блог компании Skillfactory, Веб-разработка, Программирование, TypeScript, SvelteJS]11200
990681784mozg3000tm2022-08-10 09:38:30+00:00Простое REST api для сайта на php хостингеhttps://habr.com/ru/articles/681784/NaN1028000[Веб-разработка, PHP, Программирование, API]43622
991681422aio3502022-08-10 08:44:31+00:00Заметка о полезных возможностях современного CSShttps://habr.com/ru/companies/timeweb/articles/681422/NaN1013000[Блог компании Timeweb Cloud, Веб-разработка, CSS]2111311
992681398vital_pavlenko2022-08-08 11:16:51+00:00Как сделать много форм, не сделав ни однойhttps://habr.com/ru/articles/681398/NaN89700[Веб-разработка, JavaScript, Node.JS, ReactJS]44917
993681284mr-pickles2022-08-08 09:42:36+00:00Устаревшие Python-библиотеки, с которыми пора попрощатьсяhttps://habr.com/ru/companies/wunderfund/articles/681284/NaN1132000[Блог компании Wunder Fund, Веб-разработка, Python, Программирование]3316129
994679952LabEG2022-08-08 09:00:04+00:00ReCA: React Clean Architecture state managerhttps://habr.com/ru/articles/679952/NaN83000[Веб-разработка]0825
995680670aio3502022-08-04 08:48:41+00:00Разрабатываем чат с помощью Nest, React и Postgreshttps://habr.com/ru/companies/timeweb/articles/680670/NaN1715000[Блог компании Timeweb Cloud, Веб-разработка, JavaScript, Node.JS, ReactJS]510811
996680770Novak47122022-08-04 07:40:58+00:00Использование шаблонов проектирования группы GoF в Reacthttps://habr.com/ru/companies/rshb/articles/680770/NaN99600[Блог компании РСХБ.цифра (Россельхозбанк), Веб-разработка, JavaScript, Node.JS, ReactJS]65810
997680382XNadoricheva2022-08-02 09:22:31+00:00Как использовать Docker в приложениях Node.js и Reacthttps://habr.com/ru/companies/rshb/articles/680382/NaN433000[Блог компании РСХБ.цифра (Россельхозбанк), Веб-разработка, JavaScript, Node.JS, ReactJS]38917
998680240S__vet2022-08-02 09:21:40+00:00Не реактом единым: что полезно знать современному фронтенд-разработчикуhttps://habr.com/ru/companies/hexlet/articles/680240/NaN713000[Блог компании Хекслет, Веб-разработка, Программирование, IT-стандарты, Карьера в IT-индустрии]8745